Bridging the Gap: A Decade Review of Time-Series Clustering Methods

TL;DR

This paper reviews the evolution of time-series clustering methods over the past decade, from classical techniques to modern neural network approaches, providing a comprehensive taxonomy and insights for future research.

Contribution

It offers a unified taxonomy bridging traditional and deep learning methods in time-series clustering, filling a gap left by previous surveys.

Findings

Highlights key developments in time-series clustering

Provides a comprehensive taxonomy of methods

Guides future research directions

Abstract

Time series, as one of the most fundamental representations of sequential data, has been extensively studied across diverse disciplines, including computer science, biology, geology, astronomy, and environmental sciences. The advent of advanced sensing, storage, and networking technologies has resulted in high-dimensional time-series data, however, posing significant challenges for analyzing latent structures over extended temporal scales. Time-series clustering, an established unsupervised learning strategy that groups similar time series together, helps unveil hidden patterns in these complex datasets. In this survey, we trace the evolution of time-series clustering methods from classical approaches to recent advances in neural networks. While previous surveys have focused on specific methodological categories, we bridge the gap between traditional clustering methods and emerging deep learning-based algorithms, presenting a comprehensive, unified taxonomy for this research area. This survey highlights key developments and provides insights to guide future research in time-series clustering.